Computer Vision Annotator

About the role

The Computer Vision Annotator works remotely to create high‑quality labeled datasets that power computer‑vision and machine‑learning models. You will annotate images and videos with precise bounding boxes, polygons, key points, 3D cuboids and segmentation masks, ensuring consistency and accuracy across large projects.

Key responsibilities

• Draw accurate bounding boxes, polygons, polylines and 3D cuboids on visual data using tools such as CVAT, Label Studio, 3D Slicer or SageMaker Ground Truth.
• Place key points for pose estimation and perform semantic/instance segmentation to label every pixel.
• Interpret complex labeling taxonomies, identify ambiguous cases and collaborate with project managers and data scientists to refine guidelines.
• Provide feedback on annotation tools, suggest workflow improvements and help resolve edge‑case or bias‑related issues.
• Assist with basic data preparation tasks like image resizing, format conversion and sensor data synchronization.
• Track annotation KPIs (throughput, precision, recall) and participate in calibration sessions to maintain team consistency.

Required profile

• Minimum 2 years of experience in data annotation or quality assurance for computer‑vision or ML projects.
• Proven ability to apply a variety of annotation techniques (bounding boxes, polygons, key points, 3D cuboids, segmentation masks).
• Strong understanding of computer‑vision concepts such as object detection, image classification, LiDAR and point clouds.
• Experience with quality‑control processes like consensus labeling, audit sampling and inter‑annotator agreement metrics.
• Exceptional attention to detail and ability to maintain high accuracy on repetitive tasks.
• Fluent written and verbal communication in English.

Required skills

• CVAT
• Label Studio
• 3D Slicer
• Supervisely
• Amazon SageMaker Ground Truth
• Annotation techniques: bounding boxes, polygons, key points, 3D cuboids, segmentation masks
• Computer‑vision terminology (object detection, LiDAR, point clouds)